chunzhihou

In statistics, an expectation-maximization (EM) algorithm is a method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. EM is an iterative method which alternates between performing an expectation (E) step, which computes the expectation of the log-likelihood evaluated using the current estimate for the latent variables, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.

Genetic algorithm implementation

helping in simulation of some power electronic circuits

the basic space time block code techniqes

研究生学习期间，优化方法课程中无约束线性规划的DFP的matlab程序(DFP matlab program)

线性调频（LFM）脉冲压缩雷达仿真，里面有雷达信号的处理过程，包括原理(Linear FM (LFM) pulse compression radar simulation)

粒子群算法求解最优化算法的MATLAB程序！！！！(Particle swarm optimization algorithm for solving the MATLAB program! ! ! !)

使用k均值法对一组数据进行聚类，画出结果图像(Use the k-means clustering, a set of data to draw the resulting image)

Algorithms for Image Processing and Computer Vision 2nd

MachineLearning Classifican KNN-Based Clustering for Improving Social Recommender Systems